Machine learning and deep learning models for human activity recognition in security and surveillance: a review

Abstract

Human activity recognition (HAR) has received the significant attention in the field of security and surveillance due to its high potential for real-time monitoring, identifying the abnormal activities and situational awareness. HAR is able to identify the abnormal activity or behaviour patterns, which may indicate potential security risks. HAR system attempts to automatically provide the information and classification regarding activities performed in the environment by learning the data captured through sensor or video stream. The overview of existing research work in the security and surveillance area, which includes traditional, machine learning (ML) and deep learning (DL) algorithms applicable to field, is presented. The comparative analysis of different HAR techniques based on features, input source, public data sets is presented for quick understanding, and it focuses on the recent trends in HAR field. This review paper provides guidelines for the selection of appropriate algorithm, data set, performance metrics when evaluating HAR systems in the context of security and surveillance. Overall, this review aims to provide a comprehensive understanding of HAR in the field of security and surveillance and to serve as a basis for further research and development.